AoI-Energy-Spectrum Optimization in Post-Disaster Powered Communication Intelligent Network via Hierarchical Heterogeneous Graph Neural Network

TL;DR

This paper introduces a hierarchical heterogeneous graph neural network framework to optimize AoI, energy, and spectrum efficiency in a post-disaster communication network utilizing UAVs, satellites, and GBSs.

Contribution

It proposes a novel HHGNN-based optimization framework and algorithms for managing complex, dynamic post-disaster communication systems with multiple resource constraints.

Findings

The proposed scheme outperforms state-of-the-art benchmarks.

The framework effectively balances AoI, energy, and spectrum efficiency.

Expressions for AoI and stagnant AoI proportion are derived.

Abstract

This paper designs a post-disaster powered communication intelligent network (PDPCIN) to address communication disruptions caused by ground base station (GBS) failures within the post-disaster area. PDPCIN employs unmanned aerial vehicles (UAVs) to provide wireless data collection (WDC) and wireless energy transmission (WET) for affected areas and leverages low earth orbit satellites (LEO SATs) to relay UAV data to the nearest survival GBS. To ensure basic post-disaster communication while co-optimizing age of information (AoI), energy efficiency, and spectrum efficiency, intelligent synchronization-UAV (IS-UAV) architecture, AoI-based four thresholds updating (AFTU) mechanism, and Dynamic multi-LEO access (DMLA) strategy are proposed. However, three key challenges remain: time-varying task-resource imbalances, complex topology caused by multi-device scheduling, and nonlinear coupling in multidimensional metric optimization, making system optimization NP-hard. Therefore, this paper proposes a hierarchical heterogeneous graph neural networks (HHGNN) framework. It models heterogeneous device nodes and their communication relations as a hierarchical heterogeneous graph structure, integrating our defined graph sensing, exchange, and mask layer to handle the network's input, feature propagation, and output. To search appropriate number of single-LEO SATs, we propose single-LEO SAT density optimization (S-LSDO) algorithm. Finally, we compare the proposed scheme with state-of-the-art benchmarks to validate its superior collaborative optimization of AoI, energy efficiency, and spectrum efficiency. Based on this, we derive the expressions for the expected values of AoI and stagnant AoI proportion.